Some Quantile Regression Models for Zero-Inflated Continuous Data with Applications
Date of Award
7-10-2022
Publication Type
Dissertation
Degree Name
Ph.D.
Department
Mathematics and Statistics
Keywords
Continuous data, Generalized exponential, Kumaraswamy, Non-parametric model, Quantile regression, Two-part model
Supervisor
A.Hussein
Supervisor
S.Hossain
Rights
info:eu-repo/semantics/embargoedAccess
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Abstract
This thesis develops new models for quantile regression for zero-inflated, non-negative data defined on intervals of the form [0, a) where a = 1 or ∞. The first host of models proposed in this thesis rely on two parametric distributions known as Generalized exponential and Kumaraswamy distributions, respectively, for data defined in [0, ∞) and [0, 1). The second set of models are semi-parametric models in which the zeros are modeled through a logistic regression model and the positive part of the data are modeled by using the usual linear/non-linear quantile regression models. We perform intensive Monte Carlo simulations to assess the performance of all proposed methods and we illustrate the utility of the methodologies by applying them to data sets on vehicle corrosion.
Recommended Citation
Mohamed, Khirta, "Some Quantile Regression Models for Zero-Inflated Continuous Data with Applications" (2022). Electronic Theses and Dissertations. 8989.
https://scholar.uwindsor.ca/etd/8989